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In the process of calisthenics training, athletes' movements need to be monitored and analyzed. The traditional monitoring method has the problem of insufficient resolution, which limits the improvement of accuracy and reliability. In this paper, a high dimensional image super resolution model is developed by using infrared spectral imaging technology to improve the monitoring and simulation results in the process of aerobics training. In this paper, infrared spectral imaging technology combined with deep learning algorithm is used to design a high dimensional image super resolution model. Infrared spectral imaging data were collected during aerobics training, and the data were trained and learned to extract features and realize super-resolution reconstruction. Finally, the model is tested and verified to evaluate its monitoring and simulation effect in aerobics training. The experimental results show that the high-dimensional image super resolution model has good monitoring and simulation effects in aerobics training. By increasing the detail and clarity of the image, the model can provide more accurate motion analysis and motion suggestions, so as to help the trainer improve the movement skills and improve the training effect. [ABSTRACT FROM AUTHOR]

In the aerobics competition, the judge's judgment plays a key role in the athlete's performance and ranking. However, due to the existence of human factors and subjective judgments, artificial judgments are prone to errors and unfairness. Therefore, the purpose of this study is to design and implement a machine vision system based on light detection sensor, which is used to assist judges in aerobics competition. The system aims to improve the objectivity and accuracy of competition judgment and reduce the influence of subjective factors on competition results. In this paper, the optical detection sensor technology is used to obtain real-time movement data of athletes by setting sensors in different positions of the field. The sensors can recognize the athlete's body outline, posture and movement flow, and transmit the data to a computer for real-time analysis and processing. Through machine vision algorithms, the system can automatically identify and analyze the degree of completion and technical difficulty of different actions. The experimental results show that the machine vision system based on the light detection sensor can accurately capture the movements of athletes and provide reliable data for judging. Compared with traditional manual evaluation, this system has higher objectivity and accuracy, and can reduce the interference of subjective factors. [ABSTRACT FROM AUTHOR]

Aerobics is a difficult gymnastics event, which requires very high skills for athletes. At present, the traditional video analysis method is mostly used in the simulation system of aerobics training management competition, which has some problems such as low analysis efficiency and complex data processing. Therefore, the purpose of this study is to design and implement an optical imaging detection method based on data information visualization, so as to provide an efficient and accurate aerobics training management competition simulation system. In this paper, an aerobics training management competition simulation system is designed by using optical imaging technology and data information visualization method. The system monitors and records athletes' key movements in real time through camera equipment, and then transforms the monitored movement data into intuitive visual images by using data information visualization technology. The application effect of the developed optical imaging detection method based on data information visualization in the simulation system of aerobics training management competition is verified by experiments. The experimental results show that the system can effectively and accurately monitor the key movements of athletes, and display and analyze them through intuitive visual images. [ABSTRACT FROM AUTHOR]